Fitness training guidance system and method thereof

ABSTRACT

A real-time personal fitness training guidance system is described. A camera unit including one or more cameras is provided to take images of a user ready to use or using an exercise equipment. One or more current vital signs and physical activity information are derived from the captured images. Further, a personal fitness training guidance is generated for the user in accordance with the current vital signs, the physical activity information and a workout program the user is doing. To allow the user to see how he or she is doing in his/her workout, an output unit is provided to show the personal fitness training guidance to the user in real-time. An external device is also provided to generate a coaching guidance for the user.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims the benefits of U.S. provisional application No. 62/284,735, entitled “Real-time Personal Fitness Training Guidance System”, filed on Oct. 8, 2015, which is hereby incorporated by reference for all purposes.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention is generally related to the area of fitness equipment. Particularly, the present invention is related to a real-time personal fitness training guidance system and a method thereof.

2. Description of the Related Art

Existing solutions to assist users to better use fitness equipment such as treadmills have a number of limitations and fail to truly help the users become more effective in using the fitness equipment to attain their exercise goals. In the case of treadmills, a user has to use his/her two hands to press on sensor bars installed on the fitness equipment periodically to measure his/her heart rate. As a result, the exercising movements on the treadmill are restricted with two hands on the sensor bars and only a numerical reading is provided to the user. Some of the limitations in the fitness equipment currently on the market are summarized below.

1. Users face difficulties in getting their vital signs measured continuously during an exercise. Sensor bars are seen on some treadmills, but they must be constantly held on in hands to get the vital signs measured continuously, hence a user is considerably restricted when moving his hands. Existing systems that monitor the vital signs continuously require a user to wear some types of devices, such as a heart monitor strap, that communicate with the fitness equipment through wireless technologies. They require the user to consciously wear electronic devices during workout. In reality, only the most motivated users may remember to bring the devices. If the device is not worn, there would be a data gap in the workout history. Further, the devices are prone to loss or tear and wear, and may disrupt the workout experience. More importantly, there is no correlation between the vital sign data and the activities performed by a user, hence there would be no feedback that could be provided to the user on how to improve the effectiveness of the workout.

2. A fitness equipment cannot seamlessly detect a user's profile (gender, age, body mass, etc.). Each time a user gets on a machine, the user needs to re-enter his/her profile information or take the hassle to hook up a mobile phone if he/she has already registered and the machine is network connected. In other words, the user has to do some work each time he/she uses an exercise machine to get recognized and the machine cannot identify the user nor do profiling automatically. Further, these existing systems cannot measure the physical body changes of the user.

3. User activities on a fitness equipment are not monitored. Existing systems cannot detect what activities the user is doing on a fitness equipment or get any feedback on how the user is feeling or doing during a workout.

4. No exercise history records are shared across an exercise equipment. Most often the results data are not persistently stored by the fitness equipment after the workout is done. If wearable devices are used, the results data can only be stored in the online repository of a user account but disconnected from the fitness equipment. The fitness equipment cannot communicate with each other to recognize the user identity and continue the tracking from where the user has left off before.

5. Feedback on how to improve the exercises is not provided by an existing exercise equipment. Users do not tangibly feel the impact of a workout exercise towards his/her exercise goals. There are no insightful interpretations on workout data results to feed back to a user during and after the exercises.

Accordingly, there is a great need for a fitness equipment or system that provides a user with a training guide based on how the user is doing at the moment in view of his/her past exercises.

BRIEF SUMMARY OF THE INVENTION

This section is for the purpose of summarizing some aspects of the present invention and to briefly introduce some preferred embodiments. Simplifications or omissions may be made to avoid obscuring the purpose of the section. Such simplifications or omissions are not intended to limit the scope of the present invention.

In general, the present invention is related to real-time personal fitness training guidance system and method for the same, where such a system overcomes at least one of aforementioned problems. According to one aspect of the present invention, a real-time personal fitness training guidance system is described and is able to take images of a user ready to use or using an exercise equipment. One or more current vital signs and physical activity information are derived from the captured images. Further, a personal fitness training guidance is generated for the user in accordance with the current vital signs, the physical activity information and a workout program the user is doing. To allow the user to see how he or she is doing in his/her workout, an output unit is provided to show the personal fitness training guidance to the user in real-time.

According to another aspect of the present application, a computer vision system is provided to profile the user seamlessly and automatically in view of the fitness equipment and a workout program of the user is doing. The profile of the user is also derived from captured images. The derived profile is retained or used to update a stored profile of the user, and subsequently used for generating the personal fitness training guidance.

According to still another aspect of the present application, a user does not need to wear wearable devices or press buttons periodically during a workout. As a result, the user experience of using a fitness equipment during a workout is maximized. In addition, the present invention not only displays the measured vital signs to users but also provides the user personal fitness training guidance on the basis of the current vital signs, the physical activity information and the workout program being used.

According to still another aspect of the present application, a fitness training guidance system utilizes an external device to record continuous workout data for users, which is helpful for the users to measure their performance, and achieve their workout goals. Depending on the implementation, the external device may be a part of cloud computing system or a mobile device communicating with a server.

According to yet another aspect of the present application, as part of the fitness training guidance system, a server or a collection of such servers are provided to serve as persistent storage and central processing for a plurality of users who have signed up with the system, receive user workout data from a processing unit, store the data persistently for each of the users, generate some statistics data on historic user workout data from multiple users and previously known population data sets, index user profiles through facial and physical features, generate coaching parameters as an input to a coaching module which in turn generates coaching guidance that is sent to the corresponding processing unit for display to the user.

The present invention may be implemented in numerous ways, including a method, a system, a device or a part of a system, each yielding different benefits, objects and advantages. According to one embodiment, the present invention is a personal fitness training system comprising: a fitness equipment including a camera unit with at least one camera provided to capture images of a user using the fitness equipment; a data processor, coupled to the camera unit, receiving the captured images to derive therefrom vital signs of the user exercising with the fitness equipment; and a console providing a training guidance to the user to achieve at least one goal through an exercise with the fitness equipment, wherein the guidance is personalized per the user and generated in the data processor in accordance with a profile of the user and a plurality of attributes about the user derived from the captured images. The personal fitness training system further comprises at least one server, located remotely with respect to the fitness equipment, communicating with the processing unit over a data network to receive the profile of the user and the attributes about the user for an exercising session. The server is configured to archive historic data for each of users registered therewith, the historic data for the user is updated with the profile of the user and the attributes about the user for the exercising session.

According to another embodiment, the present invention is a method for a personal fitness training system, the method comprises: capturing in real-time images of a user doing a workout on the fitness equipment; analyzing the captured images to obtain one or more vital signs and physical activity information of the user; obtaining a workout program the user is doing; generating a personal fitness training guidance for the user based on the vital signs, the physical activity information and the workout program; and delivering the personal fitness training guidance to the user.

Accordingly, one of the objects of the present inventions is to provide a mechanism to free a user exercising on a fitness equipment from any restriction, identify the user and measure the vital signs of the user without any intervention from the user, and subsequently provide the user with an effective fitness training guidance specifically personalized for the user.

Other objects, features, and advantages of the present invention will become apparent upon examining the following detailed description of an embodiment thereof, taken in conjunction with the attached drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention will be readily understood by the following detailed description in conjunction with the accompanying drawings, wherein like reference numerals designate like structural elements, and in which:

FIG. 1 shows a diagram of a complete exercise system which includes a real-time personal fitness training guidance system;

FIG. 2 shows a block figure of a local processing unit according to an embodiment of the present invention;

FIG. 3 shows a block diagram of the cloud processing unit;

FIG. 4A shows a detailed flowchart or process of providing an exercise guidance to a user according to one embodiment of the present invention;

FIG. 4B shows a detailed flowchart or process of recognizing the user;

FIG. 4C shows a detailed flowchart or process of profiling the user;

FIG. 4D shows a detailed flowchart of guiding a user in exercising on an exercising equipment; and

FIG. 5 show an overall functional diagram with exemplary data flows according to one embodiment of the present invention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

The detailed description of the present invention is presented largely in terms of procedures, steps, logic blocks, processing, or other symbolic representations that directly or indirectly resemble the operations of data processing devices. These descriptions and representations are typically used by those skilled in the art to most effectively convey the substance of their work to others skilled in the art. Numerous specific details are set forth in order to provide a thorough understanding of the present invention. However, it will become obvious to those skilled in the art that the present invention may be practiced without these specific details. In other instances, well known methods, procedures, components, and circuitry have not been described in detail to avoid unnecessarily obscuring aspects of the present invention.

Reference herein to “one embodiment” or “an embodiment” means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the invention. The appearances of the phrase “in one embodiment” in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments.

The present invention pertains to a system, a method, a platform and an application each of which is uniquely designed, implemented or configured to use images of a user ready to use a fitness equipment or exercising with the fitness equipment. As used herein, any pronoun references to gender (e.g., he, him, she, her, etc.) are meant to be gender-neutral. Unless otherwise explicitly stated, the use of the pronoun “he”, “his” or “him” hereinafter is only for administrative clarity and convenience. Additionally, any use of the singular or to the plural shall also be construed to refer to the plural or to the singular, respectively, as warranted by the context.

To facilitate the description of the present invention, a treadmill is used as an example to illustrate how one embodiment of the present invention can be applied thereto. Those skilled in the art shall appreciate that the present invention can be applied to other exercise devices, applications and common platforms. Embodiments of the present invention are discussed herein with reference to FIGS. 1-5. However, those skilled in the art will readily appreciate that the detailed description given herein with respect to these figures is for explanatory purposes only as the invention extends beyond these limited embodiments.

Referring now to the drawings, in which like numerals refer to like parts throughout the several views. FIG. 1 shows a configuration diagram of an exemplary personal fitness training system according to an embodiment of the present application. As shown in FIG. 1, the personal fitness training guidance system includes a fitness equipment 120 (e.g., a treadmill), a local processing unit 100 embedded in, affixed to or provided to a console 121, and a server or a cloud processing unit 200. it should be noted that the server or a cloud processing unit 200 is not part of the fitness equipment 120 but is provided in the personal fitness training guidance system to achieve one or more of the benefits, advantages and objectives in the present invention. According to one embodiment, the console 121 is shown in FIG. 1 to include a user interface or controls and coupled to the local processing unit 100 that includes a camera unit 101, a data processor 102, and a display 103. The data processor is designed or programmed to implement or perform some or all of the functions contemplated in the present invention, with or without an external device (e.g., a server or a mobile device).

Depending on the embodiment, the console 121 may be situated in, on or near the fitness equipment 120. In the example of FIG. 1, the console 121 is provided in the front of a treadmill, the camera unit 101 is also disposed in front of the treadmill to take real-time images of a user exercising on the treadmill, the data processor 102 is designed or programmed to derive user workout data (including current vital signs and physical activity information, which will be described in detail later) from the captured images, and provide in real-time the personal fitness training guidance to the user based on the user workout data; The data processor 102 may optionally receive some workout data from the exercise equipment itself. For example, it could receive speed, incline, resistance directly from the treadmill. The data may be processed locally or transported to the server 200 for further processing.

In one embodiment, the server 200 is located remotely with respect to the fitness equipment 120 but in communication with the processing unit 100 over a data network (e.g., the Internet, wired or wireless network). As part of the fitness training guidance system, the server or a collection of such servers 200 serve as persistent storage and central processing for a plurality of users who have signed up with the system, receive the user workout data from a processing unit, store the data persistently for each of the users, generate some statistics data on historic user workout data from multiple users and previously known population data sets, index user profiles through facial and physical features, generate coaching parameters as an input to a coaching module which in turn generates coaching guidance that is sent to the corresponding processing unit for display to the user. In one embodiment, the coaching parameters refer to all physiological and biomechanical data derived from the profile, exercise responses and fitness level associated with a user while he is doing an exercise with the system. It should be noted that a single cloud processing instance can support or connect with many entities of local processing units.

Referring now to FIG. 2, it shows a functional block diagram of an exemplary local processing unit 100 according to an embodiment of the present invention. As shown in FIG. 2, the local processing unit 100 comprises: a camera unit 101 configured to take in real-time images of the user doing a workout at the fitness equipment. The camera unit 101 includes at least one camera to capture images that include the user's head. The types of the camera can include visual light cameras, infrared cameras, depth sensors, or sensors which capture in other regions of the spectrum (visible or invisible), and provide a sequence of data in time that can be indexed by a set of two or more coordinates (e.g. horizontal and vertical coordinates). As shown in FIG. 1, the camera unit 101 may include additional cameras that capture images of other areas of the user's body. For example, an additional camera can take images of the user's lower body, including the legs so that additional information about the user's workout activities can be determined.

The data processor 102 communicating with the camera unit 101 receives the captured images therefrom, performs real-time analysis of the images to obtain one or more current vital signs (e.g. a heart rate or a body temperature) and physical activity information (e.g., information indicating whether the user is running, walking or resting) of the user, and generates in real-time a personal fitness training guidance message (e.g., prompting the user to increase his speed of movement) for the user on the basis of the current vital signs, the physical activity information and a workout program the user is doing. While the derived current vital signs and the physical activity information are used to generate the personal fitness training guidance message, they are sent to the cloud processing unit 200 for record and statistical analysis. The data processor 102 can also receive status information from the exercise equipment that includes the state of the machine, such as speed incline, resistance, and etc.

An output unit 103 (e.g., a display) communicating with the local data processor 102 receives the personal fitness training guidance message from the local data processor 102 and provides in real-time the personal fitness training guidance message to the user. The output unit could be a screen or any other kind of visual display, or speakers, headphones, or any other kind of video/auditory output device.

In one embodiment, the local processing unit 100 is integrated with the exercise equipment in which case the camera unit 101 is part of the console on the exercise equipment. Depending on the implementation, the local processing unit 100 can be a separate unit internally to the console, or the functions could be implemented on an existing processing unit in the exercise equipment, and the output messages would be output on the exercise equipment display.

FIG. 3 shows a functional block of a cloud processing unit 200 according to an embodiment of the present application. As shown in FIG. 3, the cloud processing unit 200 includes a tracking agent or module 201, a coaching agent or module 203, and an analytic agent or module 205. Depending on the implementation, each of the modules 201, 203 and 205 may be implemented in software or a combination of software and hardware.

The tracking module 201 is designed to process requests to store or retrieve historic exercise and profile data for all users registered in the system. The data is persistently stored in a user database 202 which contains the historic data of all exercise records by a registered user. For example, it stores vital sign readings, body form readings, equipment statuses, guidance messages by a time line (e.g., second, minute, hour or day).

The coaching module 203 manages a set of personalized workout instructions for each workout session. It loads a workout program template from a Workout Program Database 204 and customizes it based on inputs from the local processing unit 100. The workout program is referred to a program for training an exercising user and customized for the user by the real-time personal fitness training guidance system. According to one embodiment, a workout program is a collection of one or more training phases. For example, there could be 3 phases, a warm-up phase, a main phase, a cool-down phase, in a workout program. The goals of each phase in terms of the activities, and the vital sign limits are customized for each user based on the profile information, and the user's goals. The phase goals include but are not limited to duration, distance, physiological limit (e.g., heart rate), the number of repetitions, and etc. A goal can also be in the form of a virtual score derived from tangible data listed above. The coaching module 203 provides a guidance to the user in achieving each of the phase goals. When a phase goal is met or a time limit has passed, the coaching module 203 guides the user to move to the next phase.

The analytic engine 205 periodically analyzes the historical workout database and derives correlations between experience types, intensities, user profile types, and vital sign measures based on machine learning techniques. It is used to predict an exercise response and personal preference of a user based on the behavior of other users who share similar physical or behavioral attributes. The exercise response is referred to all physiological and biomechanical changes on the user's body right after performing a certain exercise. These exercise-response pattern insights are stored in Population Segment Database 206 which stores the exercise-response data and preference of each of the users. The Population Segment Database 206 also stores general population data sets, exercise best practices that would be applicable to each of them.

The above is an introduction of the components of the real-time personal fitness training guidance system, the local processing unit 100, the cloud processing unit 200 and the signal transmission relationship among them according to an embodiment. Below is a description of a workflow of the real-time personal fitness training guidance system according to one embodiment of the present application with respect to FIG. 4A. FIG. 4B, FIG. 4C, and FIG. 4D are the expanded details of FIG. 4A.

Referring now to FIG. 4A, it shows a flowchart or process 450 of providing an exercise guidance to a user. Depending on implementation, the process 450 may be implemented in software or a combination of hardware and software. At 300, the process 450 moves to 300 to recognize the user, namely one or more cameras are put into a working mode to take images of a user ready to use or while using the exercise equipment.

FIG. 4B shows a flowchart or process 460 of recognizing the user. At 400, the system profiles the user from these images. At 301, a camera unit or one of the cameras therein is positioned to monitor the location where a user shall appear to use the fitness equipment. Until it recognizes that a user shows up, the system may remain idle or shows a promotional video and audio messages to promote the system to potential users walking by the system. At 302, the camera unit recognizes that a user is on the fitness equipment 120. The user detection or recognition can be implemented using face detection from facial images (when the camera is focused on the facial part of the user). Detecting a human face within a certain defined region in the field of view of the camera unit can be used as an indication that a user is ready to use the exercise equipment. When a user is detected, the system or the console is caused to prompt the user with a welcome message at 303. The welcome message includes a call-to-action message that asks the user if they wish to use the exercise guidance system.

At 304, the system waits for the user to agree to use the guidance system. If the user agrees, the exercise guidance system proceeds to 305. Otherwise the system won't start, and the user is just using the exercise equipment as usual without a real-time guidance generated based on his state. To provide more personalized training guide, some inputs from the user to the system can be through a variety of methods, including but not limited to a gesture input through the camera sub-system, a voice input, or inputs via a touch screen. Eventually, the process 460 goes to 305 that returns to 300 of FIG. 4A. The process 350 of FIG. 4A now goes to 400 to profile the user to establish the context for executing the exercise program, which is further detailed in FIG. 4C.

Referring now to FIG. 4C, it shows a flowchart or process 470 of profiling the user. It is assumed that the user agrees to use the guidance system. The profile of a user is referred to some basic information (e.g., age, gender, weight, and etc.) of the user which is useful in generating a set of coaching parameters for the user. The profile also contains an ID of the user which could be generated based on extracting facial attributes from images of the user's face.

At 401, the camera unit is driven to capture images of the user positioned at the fitness equipment. At 402, the local data processor detects a predefined image region in one or more of the captured images that maps to the face of the user. The local data processor sends detected facial image regions to the tracking module 201 of FIG. 3, for example. The tracking module 201 uses a face recognition technique based on the captured image regions and data stored in the user database 202 to recognize the known users. In other words, users who have previously used the system, and who have recorded profile information, and exercise histories can be confirmed as a returning user. When there is a match of user ID with the user, the profile of the user corresponding to the identity of the user is obtained from the tracking module for subsequent exercise guidance processing.

In the event that the user is not recognized, the user is treated as a new user. Using the face region as an input, at 404, the local processor unit estimates the birth year of the user. At 405, a gender analyzer is designed to analyze the face region of the image to estimate the gender (e.g., male or female) of the user. There are a variety of methods known in the art for estimating age and gender from facial images. These methods all use sophisticated machine learning methods that have been trained on large databases of labeled data, and their reliability is sufficient for the system to use to derive initial coaching parameters for the exercise guidance system.

At 406, the camera unit 101 take images of the body of the users. These images are used to determine measures of physical attributes of the user such as weight and height. The physical attribute refers to the appearance characteristics or biological characteristics of the users including body shape, body movement, age, weight, and gender. These attributes can be objectively measured but approximately inferable from the appearance of the user. The physical attributes can be used to more accurately determine certain workout data such as calories burnt, and also helps to determine the fitness level changes of the user over time. There are a variety of methods known in the art for estimating physical attributes. For example, if the camera system includes depth cameras, a full body outline with dimensions can be extracted from the images, and from this outline, the height and weight can be fairly estimated. If a camera sub-system includes only RGB cameras, it is still possible to estimate physical attributes using multiple cameras, when the locations of the cameras relative to each other are known, and the cameras have overlapping views.

At 407, the exercise profile information is created for this new user. The new profile is stored in the user database 202 of FIG. 3. If this user returns to use the system next time, his record can be determined already in existence and retrieved in 408.

The user's profile information is used to prepare for coaching parameters in 409 and are further used in exercise guidance subsequently. The coaching parameters that are derived include the maximum heart rate and heart rates zones. There are well known methods that take gender and age into consideration when providing estimates of the maximum heart rate. For example, one gender independent formula is to use 220−age to derive the maximum heart rate. More sophisticated formula can include gender and weight. For example, if gender is included, one common formula is to use 220−age for men, and 226−age for women. If weight is included, another popular formula is 216−0.5*age−0.05*weight (in lbs), or for women, 211−0.5*age−0.05*weight (in lbs). Heart rate zones can be determined from the maximum heart rate. These heart rate zones are important in the exercise guidance to ensure that the user is working at the correct intensity to make improvements according to their goals. For example, an endurance aerobic zone can be estimated as 50-60% of the maximum heart rate. Exercising in this zone is excellent for losing weight and increasing aerobic endurance. The threshold zone which is important for improving running economy, and pushing the limits of running speed for 5K and 10K races is 80-90% of the maximum heart rate. Recovery heart rate zone, is <50% of maximum heart rate, is also important for resting between the intense intervals in an interval workout.

At 410, the profile of the user from 408 and the coaching parameters are used to create a list of potential workout programs, from the list of workout programs stored in the workout program database 204. Different workout programs are catered to different fitness goals (e.g., losing weight, toning up, getting stronger, or gaining muscle mass). The user can make a selection from the list. If the user does not select a program, the system is programmed or designed to select a workout program for the user based on his profile information. This workout program along with the user's profile, and the coaching parameters are used for the system to generate a training guidance.

After the user profile has been derived and the workout program is selected, according to 400 of FIG. 4A, the system starts the guidance according to 500 of FIG. 4A. Referring now to FIG. 4D, it shows a flowchart or process 480 of producing a training guidance to the user. At 501, the local data processor 102 captures images from the camera unit 101. These images are used to estimate vital signs at 502, and extract what activities the user is doing at 503.

According to one embodiment, at 502 the system uses the images captured to estimate the user's vital signs and sends the vital signs to the coaching module 203. The vital signs in the embodiment includes a heart rate, a respiration rate and an blood oxygen level. Estimation of the heart rate from the images is known as remote photoplethysmography. It uses RGB data from the cameras to sense the periodic changes in blood volume in the face that reflects the periodic beating of the heart. The user's breathing rate can also be determined by observing the movement of the chest. Periodic motions of the chest correspond to the periodic breathing patterns of the user. With these methods it is possible to obtain a user's vital signs continuously without any wearable sensor, and without requiring the user to touch any sensor on the exercise equipment.

At 503, the system uses the images to extract the physical activity the user is performing and sends these activity measurements to the coaching module 203. The physical activity measurements sent to the coaching module can include information obtained from the exercise equipment, as well as information derived by processing the captured images. The physical activity information in one embodiment includes information showing an exercise form, an intensity level, a cadence, a speed, a distance and etc. For example, the user's cadence can be estimated by tracking the position of the user's face within the field of view of the camera unit. The periodic motion of the face can be used to derive the running or walking cadence of the user. The motion of the user's head during running can also be used to estimate bounce, which is the vertical motion that the user undergoes during their running pattern. Bounce is an indication of the efficiency of the running technique. Lower bounce is more efficient. If the camera unit includes a field of view of the user's feet, the system can also estimate a time of ground contact. Lower ground contact time is an indicative of a more efficient running style. If the camera unit includes a depth camera sensor, then the complete skeleton of the user in running can be extracted, and such metrics as posture, including body lean and footfall can be derived.

At 504, the coaching parameters from 409 are processed. The coaching module 203 takes the coaching parameters and the workout programs to generate the guidance for the user to complete an exercise. When the user has no workout history in the system, the coaching parameters are derived from the most basic profile information which includes age, gender, body weight as described earlier. As the user exercises under the guidance, the system tracks the user's exercise responses over time, and will include these exercise responses when deriving the coaching parameters for the coaching module 203 to use in guiding the user. An exercise response from the user is derived at 505 by continuously monitoring the user's vital signs and the exercise activities.

Once some exercise response data are accumulated over the time, the analytic module can assess a fitness level for the user. Because the physical attributes of different users with different profiles (e.g. different ages, different genders) are all input for the fitness level assessment, the analytic module 205 can obtain predicted physical attributes of users with the same or similar profiles, e.g. in the same age group (e.g., in 41-50 year-old period), in the same gender, in the same weight interval (e.g. in 60-65 kg weight interval), and then assess the fitness level of the user based on comparison between the obtained exercise response of the user and the predicted physical attributes of other users with the similar profile. As more and more physical attributes are input to the fitness level assess model, the accuracy of assess of the fitness level assess model increases.

At 505, the coaching module takes the derived vital signs, the activity information, the coaching parameters derived from the user profile, the physical attributes of the user, and the workout program, and generates a guidance for the user to complete the workout program. A workout program includes one or more phases and each phase defines one or more goals. The goals include but are not limited to a duration, a distance, a physiological limit (e.g., a heart rate), the number of repetitions, and etc. The goal can also be in the form of a virtual score derived from the tangible data listed above. The coaching module 203 provides the guidance to the user in achieving the goals within a phase. When the goal for a phase is met or a time limit has passed, the coaching module 203 guides the user to move to the next phase.

The coaching module 203 generates in real-time a personal fitness training guidance message for the user on the basis of the current vital signs, the physical activity information and the workout program. The guidance messages are referred to as instructions that clearly describe how the user should change the current settings of the exercise equipment (e.g., “Short break. Let's drop the speed to 3”), the exercise behavior (e.g. “Try to speed up your cadence to 180”), a body form (e.g. “Keep your body straight up”), or a statement that instructs the user what to do with the current exercise and the physiological state of the user (e.g., “Just reached the target zone. Let's keep it there”), or statement that encourages the user to attain higher exercise performance (e.g., “Use the last 30 seconds for a hard push, increase speed by half a mile or more. You can do it! It is only for 30 seconds”). The guidance messages are personalized for the user and based on the predicted exercise responses that the coaching module learns from the real-time and historical workout data of the user and the general population. For example, from the physical activity information, the speed of the user is slow and, the current vital sign of the user is still 10% below the lower limit of the heart rate zone (e.g., in fat burning zone). The coaching module is designed to know that the user selects a weight loss program. If the user can exercise in the fat burning zone for 20 minutes every day, it can help the user to reach the fitness goal in one month. In one embodiment, with the profile of the user, the past workout history and general population data sets, the coaching module is designed to predict that the user can reach the fat burning zone if the user increases the treadmill speed by 0.5 mph. Then, the local data processor 102 generates a personal fitness training guidance message to instruct the user to increase the speed by 0.5 mph so that the heart rate can reach the desired fat burning intensity zone. The coaching module also continuously generates messages to encourage the user to keep exercising at speed levels that maintains the heart rate for 20 minutes. This makes the exercise much more effective and predictable in helping the user to achieving his goals.

In addition. the exercise activities can be inferred to determine the exercise form of the user such as the cadence, the footfall, the lean, and bounce. It can be recognized from the captured images. The coaching module can have an exercise form target to guide the user to follow. The system can generate an action correction coaching parameter based on comparison of the exercise form target and the body form of the user. If the exercise form target does not match the body form of the user, the action correction message instructs to the user to stop the current exercise form and advises the user another exercise form which matches the body form of the user most. For example, it is well recognized that an efficient cadence for running is close to 180 steps per minute, while most beginning runners have a cadence well below this target. The system can guide the user to increase their cadence if it is below the target, and so help them improve their running efficiency.

The body form guidance can be done by analyzing the full body motion images of the users, possibly using multiple RGB or depth cameras so that a full 3D view of the user exercising can be derived. This 3D view can be displayed so that the user can feel he is exercising in front of a mirror. The system evaluates the running form against some best practice benchmark rules. For example the system can monitor the footfall position and guide the user toward midfoot striking (foot landing right underneath the body) as often as possible. The system can monitor posture, and guide the user to have the correct slight forward leaning posture.

The guidance message is provided to the user through the output unit 103. If the output unit 103 is a screen, the output unit 103 can display the personal fitness training guidance message of the user on the screen. If the output unit 103 is a speaker, the output unit 103 can output a voice of the personal fitness training guidance message.

While the user is exercising under the guidance of the coaching module, the system will continuously monitor the exercise activities, and vital signs at 506. By this continuous monitoring, the system can derive the vital signs vs. exercise intensity response, and at the same time use the measurements, along with known population data sets, to determine various measures of the user's performance, including his fitness level. The fitness level can be quantified in metrics like VO2Max. The fitness level can be measured based on vital sign responses relative to the exercise intensity. One way to derive fitness level is to use the stable heart rate of the user at a predetermined running speed and incline. Alternatively, the same method plus the user's physical attributes, such as age and gender, can be used. The fitness level can be used to predict an exercise race performance. A good training program should be based upon the current fitness level of the user, and should adapt as the fitness of the user changes. The analytic module 205 can compare the user's exercise response with comparable users' historical exercise data. Updated fitness level can be generated to add to the coaching parameter list.

At 508, after the user completes the workout program, the user is shown with the workout results. Based on the fitness performance evaluation, the system can rank the user's fitness performance with the general public. For example, based on the running pace and the vital signs response, the system can estimate the pace of the user at various standard race distance (5K, 10K, half-marathon, marathon, etc.). The system can also estimate their race ranking according to published records of races. The user can see a summary of their total workout efforts in terms of calories burnt, and how much it accounts for based on the government-published weekly exercise amount guideline.

At 509, the user indicates if he wants the system to store his workout records. If the user permits, the workout records are sent to the tracking module for storage at 510. The user input to the system, can be through a variety of methods, including but not limited to a gesture input through the camera sub-system, a voice input, or interactions via a touch screen.

Referring now to FIG. 5, it shows a systemic diagram 550 with exemplary data flows among different devices according to one embodiment of the present invention. Besides a fitness equipment 552 (shown as a user using the equipment), a local processing unit 554 is designed to recognize the user from a certain set of images, derive the vital signs of the user from another set of images and detect activities from still another set of images. Based on these obtained data, a guidance is generated by the local processing unit 554. Meanwhile, the workout record collected in the local processing unit 554 is transported to a cloud computing device 556 that is provided to store records for all registered users. The cloud computing device 556 can be programmed to perform various statistics based on different groups (e.g., an age group, a gender group, and a region group), where the statistics may be shared with other fitness club or a 3rd party 558 (e.g., an advertiser).

In addition, as shown in FIG. 5, a personal device (e.g., a smartphone) 560 may be used to sign in the user when he is ready to use a fitness equipment. The fitness goal of the user may be stored in the personal device 560 and loaded to the local processing unit 554 to generate or modify the fitness training guidance.

The invention is preferably implemented by software, but can also be implemented in hardware or a combination of hardware and software. The invention can also be embodied as computer readable code on a computer readable medium. The computer readable medium is any data storage device that can store data which can thereafter be read by a computer system. Examples of the computer readable medium include read-only memory, random-access memory, CD-ROMs, DVDs, magnetic tape, optical data storage devices, and carrier waves. The computer readable medium can also be distributed over network-coupled computer systems so that the computer readable code is stored and executed in a distributed fashion.

The present invention has been described in sufficient details with a certain degree of particularity. It is understood to those skilled in the art that the present disclosure of embodiments has been made by way of examples only and that numerous changes in the arrangement and combination of parts may be resorted without departing from the spirit and scope of the invention as claimed. Accordingly, the scope of the present invention is defined by the appended claims rather than the foregoing description of embodiments. 

We claim:
 1. A personal fitness training system comprising: a fitness equipment including: a camera unit with at least one camera provided to capture images of a user using the fitness equipment; a data processor, coupled to the camera unit, receiving the captured images to derive from the captured images one or more vital signs of the user exercising with the fitness equipment; and a console providing a training guidance to the user to achieve at least one goal through an exercise with the fitness equipment, wherein the guidance is personalized per the user in accordance with a plurality of attributes about the user derived from the captured images.
 2. The personal fitness training system as recited in claim 1, wherein the training guidance is generated in accordance with a profile of the user.
 3. The personal fitness training system as recited in claim 2, wherein the data processor is configured to detect a presence of the user when the user is ready to use the fitness equipment.
 4. The personal fitness training system as recited in claim 3, wherein the data processor is configured to profile the user from the images.
 5. The personal fitness training system as recited in claim 1, wherein the camera unit is further positioned to take images of the user so that the data processor detects workout activities being performed by the user.
 6. The personal fitness training system as recited in claim 5, wherein the training guidance is personalized to the user based on the one or more vital signs and the workout activities.
 7. The personal fitness training system as recited in claim 6, wherein the data processor receives status information from the fitness equipment, the status information includes at least one of: speed, incline, and resistance.
 8. The personal fitness training system as recited in claim 7, wherein the training guidance is personalized to the user based on the one or more vital signs, the workout activities and the status information.
 9. The personal fitness training system as recited in claim 6, further comprising: at least one server, located remotely with respect to the fitness equipment, communicating with the data processor over a data network to receive the profile of the user and the attributes about the user for an exercising session, wherein the server is configured to archive historic data for each of users registered therewith, the historic data for the user is updated with the profile of the user and the attributes about the user for the exercising session.
 10. The personal fitness training system as recited in claim 9, wherein the server is coupled to or served as persistent storage and central processing for a plurality of users who have signed up with the system.
 11. The personal fitness training system as recited in claim 10, wherein the server receives user workout data from each of the users when the each of the users uses the system, stores the workout data persistently for each of the users, generates statistics data on historic user workout data from the users, indexes user profiles through facial and physical features.
 12. The personal fitness training system as recited in claim 11, wherein the training guidance is personalized to the user based on the one or more vital signs, the workout activities, and the statistics data.
 13. The personal fitness training system as recited in claim 11, wherein the server further generates coaching parameters as an input to a coaching module which in turn generates a coaching guidance sent to a corresponding processing unit for display to a user.
 14. The personal fitness training system as recited in claim 13, further comprising a mobile device communicating with the server, the mobile device receives the coaching guidance from the server over a data network.
 15. The personal fitness training system as recited in claim 9, wherein the server includes a coaching module designed to customize a coaching guidance for the user based on inputs from the data processor, an analytic engine periodically analyzing a historical workout database to derive correlations between experience types, intensities, user profile types, and vital sign measures based on machine learning techniques.
 16. A method for providing a training guidance in a personal fitness training system, the method comprising: capturing by a camera unit in real-time images of a user doing a workout on the fitness equipment; analyzing the captured images to obtain one or more vital signs of the user; obtaining a workout program the user is doing; generating a personal fitness training guidance for the user based on the vital signs, the physical activity information and the workout program; and delivering the personal fitness training guidance to the user.
 17. The method as recited in claim 16, wherein the camera unit includes at least one camera or is repositioned to image different parts of the user.
 18. The method as recited in claim 17, further comprising: profiling the user from a set of facial images from the camera unit; and detecting workout activities being performed by the user from a set of images.
 19. The method as recited in claim 18, further comprising: determining a fitness level of the user; obtaining a fitness goal of the user; and generating a workout program for the user based on the fitness level of the user and the fitness goal of the user in accordance with the status information.
 20. The method as recited in claim 18, wherein said profiling the user from a set of facial images comprises: determining an identity of the user from the facial images; and obtaining the profile of the user corresponding to the identity to indicate that the user is a returning user. 